Pipe feature identification using pipe inspection data analysis

ABSTRACT

One aspect provides a method, including: operating a mobile pipe inspection platform to obtain sensor data for the interior of a pipe; analyzing, using a processor, the sensor data using a trained model, where the trained model is trained using a dataset including sensor data of pipe interiors and one or more of: metadata identifying pipe feature locations contained within the sensor data of the dataset and metadata classifying pipe features contained within the sensor data of the dataset; performing one or more of: identifying, using a processor, a pipe feature location within the sensor data; and classifying, using a processor, a pipe feature of the sensor data; and thereafter producing, using a processor, an output including one or more of an indication of the identifying and an indication of the classifying. Other aspects are described and claimed.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Patent Application Ser. No.62/583,694, having the same title and filed on Nov. 9, 2017, thecontents of which are incorporated by reference in their entiretyherein.

FIELD

The subject matter described herein relates to collection and use ofsensor data for underground infrastructure such as a pipe or pipenetwork, and specifically relates to pipe feature identification and/orclassification using computer vision or image analysis techniques.

BACKGROUND

Underground infrastructure such as pipe networks for municipalities thatcarry potable water, waste water, etc., need to be inspected andmaintained. Pipes are often inspected as a matter of routine upkeep orin response to a noticed issue.

Various systems and methods exist to gather pipe inspection data. Forexample, pipe inspection data may be obtained by using closed circuittelevision (CCTV) cameras or using a mobile pipe inspection robot. Forexample, a mobile pipe inspection platform traverses through theinterior of a pipe and obtains sensor data regarding the interior of thepipe, e.g., visual image data and other sensor data for visualizing pipefeatures such as pipe defects, root intrusions, etc. Typically, aninspection crew is deployed to a location and individual pipe segmentsare inspected, often individually in a serial fashion, in order tocollect pipe data and analyze it.

BRIEF SUMMARY

In summary, one aspect provides a method, comprising: operating a mobilepipe inspection platform to obtain sensor data for the interior of apipe; analyzing, using a processor, the sensor data using a trainedmodel, wherein the trained model is trained using a dataset includingsensor data of pipe interiors and one or more of: metadata identifyingpipe feature locations contained within the sensor data of the datasetand metadata classifying pipe features contained within the sensor dataof the dataset; performing one or more of: identifying, using aprocessor, a pipe feature location within the sensor data; andclassifying, using a processor, a pipe feature of the sensor data; andthereafter producing, using a processor, an output including one or moreof an indication of the identifying and an indication of theclassifying.

Another aspect provides a system, comprising: a mobile pipe inspectionplatform that comprises one or more sensors that obtain sensor data forthe interior of a pipe; and a computer system operatively coupled to themobile pipe inspection platform and configured to: analyze the sensordata using a trained model, wherein the trained model is trained using adataset including sensor data of pipe interiors and one or more of:metadata identifying pipe feature locations contained within the sensordata of the dataset and metadata classifying pipe features containedwithin the sensor data of the dataset; perform one or more of: identifya pipe feature location within the sensor data; and classify a pipefeature of the sensor data; and thereafter produce an output includingan indication of the pipe feature.

A further aspect provides a product, comprising: a non-transitorystorage device that stores code that is executable by a processor, thecode comprising: code that analyzes sensor data obtained from a mobilepipe inspection platform using a trained model, wherein the trainedmodel is trained using a dataset including sensor data of pipe interiorsand one or more of: metadata identifying pipe feature locationscontained within the sensor data of the dataset and metadata classifyingpipe features contained within the sensor data of the dataset; code thatperforms one or more of: identifying a pipe feature location within thesensor data; and classifying a pipe feature of the sensor data; and codethat thereafter produces an output including an indication of the pipefeature.

The foregoing is a summary and is not intended to be in any waylimiting. For a better understanding of the example embodiments,reference can be made to the detailed description and the drawings. Thescope of the invention is defined by the claims.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1(A-B) illustrates example images and pipe feature detectionaccording to an embodiment.

FIG. 2(A-B) illustrates example images and pipe feature classificationaccording to an embodiment.

FIG. 3 illustrates an example method of training a system for automaticpipe feature detection and classification according to an embodiment.

FIG. 4 illustrates an example method of pipe feature identification andclassification according to an embodiment.

FIG. 5 illustrates an example computing device according to anembodiment.

DETAILED DESCRIPTION

It will be readily understood that the components of the embodiments, asgenerally described and illustrated in the figures herein, may bearranged and designed in a wide variety of ways in addition to theexamples described herein. The detailed description uses examples,represented in the figures, but these examples are not intended to limitthe scope of the claims.

Reference throughout this specification to “embodiment(s)” (or the like)means that a particular described feature or characteristic is includedin that example. This particular feature or characteristic may or maynot be claimed. This particular feature may or may not be relevant toother embodiments. For the purpose of this detailed description, eachexample might be separable from or combined with another example, i.e.,one example is not necessarily relevant to other examples.

Therefore, the described features or characteristics of the examplesgenerally may be combined in any suitable manner, although this is notrequired. In the detailed description, numerous specific details areprovided to give a thorough understanding of example embodiments. Oneskilled in the relevant art will recognize, however, that the claims canbe practiced without one or more of the specific details found in thedetailed description, or the claims can be practiced with other methods,components, etc. In other instances, well-known details are not shown ordescribed in detail to avoid obfuscation.

Sensor data (e.g., still image data, video data, laser, sound orterahertz sensor data formed into a scan or other image, etc.,collectively “sensor data”) captured by one or more sensors of a pipeinspection robot, such as visible light cameras or other sensors, may beviewed by a user to identify pipe features such as pipe defects locatedinside of a pipe (e.g., cracks, root intrusion, sediment buildup, pipewall erosion, etc.). In addition to controlling the movement of the pipeinspection robot, users are capable of remotely controlling the sensors(e.g., by utilizing pan and tilt functions, etc.) to look around andattain different visual perspectives of the pipe. The captured sensordata may be viewed on a display screen by a user located at a remotelocation.

Conventionally, when observing pipe inspection data displayed on ascreen, users may estimate the identity a potential defect or pipefeature that the user sees in the sensor data, displayed on the screenin the form of an image. This is often done by an experiencedtechnician, with knowledge of the pipe type and history, and thetechnician estimates the location and identity (e.g., type) of the pipefeature. For example, on a display screen a crack in a pipe may appearand a user can manually annotate this pipe feature. However, since thereare a variety of pipe features, the user must estimate the identities ofvarious types of defects/features for a pipe. Depending on theexperience of the users viewing the sensor data, the estimation may berelatively accurate or may be inaccurate. Experienced users may havefamiliarity with particular pipes and their corresponding dimensions,common objects found in those pipes, common defects associated with thepipes, and the like. However, due to the abundance of different pipetypes, pipe sizes, potential defects associated with those pipes, andthe like, even experienced users may be unable to provide accurateestimations regarding the identity of particular visualized pipefeatures. Moreover, there is often a vast amount of pipe inspectiondata, making manual review and annotation of pipe features timeconsuming and costly.

Accordingly, an embodiment provides for the collection and use of sensordata for underground infrastructure assets such as a pipe network usinga mobile pipe inspection platform, e.g., a tracked robot, a sledinspection platform, etc. The sensor data obtained by sensors of suchpipe inspection platforms is subject to automated computer vision orimage analysis, e.g., analysis of image features from visual images orsynthetic/constructed images such as images formed using laser scandata, acoustic data, terahertz image data, or a combination of theforegoing.

In an embodiment, an automated pipe feature detection or identificationprocess is provided by analyzing the sensor data for the interior of thepipe. In this disclosure, sensor data includes visual image data as wellas other sensor data, for example that is formed into an image. In anembodiment, machine learning, for example as provided by a neuralnetwork or other object identification technique, may be used to detect(locate) potential pipe feature(s) in the sensor data and/or to classify(identify) pipe features of the sensor data. In an embodiment, object(s)detected in sensor data reported by a pipe inspection platform may beautomatically classified or labelled using an automated technique, forexample using a neural network that is trained to identify specific pipefeatures. Further, an embodiment may make a material identification ofthe pipe feature(s) in some cases, e.g., distinguish between a crack inpipe formed of cement and rebar, identification of exposed rebar,identification of root intrusion versus sediment buildup, etc. In somecases, additional sensor data, e.g., laser scan data, terahertz imagedata, etc., may be utilized in addition to visual image data.

The description now turns to the figures. The illustrated exampleembodiments will be best understood by reference to the figures. Thefollowing description is intended only by way of example and simplyillustrates certain selected example embodiments.

Referring now to FIG. 1(A-B), example images and pipe feature detectionaccording to an embodiment are illustrated. In the image 101 a of FIG.1A, an interior of a pipe is shown in which a pipe feature 102 a ofinterest appears. The pipe feature 102 a of image 101 a is exposed rebarin the interior of the pipe wall. This pipe feature 102 a indicates thatthe interior of the pipe wall has eroded to a significant degree. Assuch, if visualized by an experienced technician, this pipe feature 102a may be located and annotated for follow up inspection, repair, etc.

An embodiment provides automated image analysis techniques that detectpipe features, such as pipe feature 102 a. For example, in an initialphase, an embodiment may detect that an object of interest is located inan area of the image 103 a. In the example of image 101 a, the pipefeature 102 a is located within the area 103 a, and an embodiment mayautomatically identify this area 103 a with a bounding box, asillustrated in FIG. 1A.

Further, an embodiment may automatically classify the pipe feature 102a, e.g., for labeling or annotating the feature automatically, ratherthan relying on a technician to manually annotate the feature. Forexample, an embodiment may subject the image 101 a, or a portionthereof, e.g., area 103 a, to further analysis in order to classify thepipe feature 102 a, e.g., as opposed to another pipe feature.

Turning to FIG. 1B, another image 101 b is illustrated, and again a pipefeature 102 b is detected within the interior of the pipe. Here, thepipe feature 102 b is a crack located in the pipe wall where a naturalseam is located. As shown, an embodiment may analyze the image toautomatically detect feature 102 b as a feature of interest, and providea bounding box 103 b indicating the location of the feature 102 b.Again, an embodiment may perform additional analysis of the pipe feature102 b, e.g., in order to distinguish it from another type pipe feature,e.g., pipe feature 102 a.

Turning to FIG. 2(A-B), an embodiment facilitates the automateddetection and annotation or labeling of the pipe features of images 201a and 201 b. By way of example, an embodiment may use an imageclassification process to automatically classify the images ascontaining features of a particular type. In an embodiment, the imageclassification may be conducted by a neural network that is trainedusing reference data, for example images of a pipe interior that areannotated manually in order to train the neural network. Thus, anembodiment may utilize a neural network to operate on an input image,e.g., image 201 a or image 201 b, in order to distinguish pipe features102 a or 102 b, from one another (and other features). Thus, anembodiment may provide an output in which an image of the pipe interior,e.g., image 201 a, 201 b, is provided with a label or annotation such as“Exposed Rebar” or “Seam Crack,” along with a bounding box or otherindication of a location of the pipe feature that generated thelabelling or annotation.

FIG. 3 illustrates an example method of training a system for automaticpipe feature detection and classification according to an embodiment. Anembodiment provides pipe feature detection that allows training ofneural networks to detect pipe features (such as cracks in a pipe wall,root intrusions, pipe erosion features such as exposed rebar, and thelike) in images (inclusive of other sensor data) and indicate, e.g.,define bounding boxes around, these pipe feature(s) automatically. Anembodiment may utilize a customized or partially customized neuralnetwork for identifying the pipe features. For example, an existingmodel architecture may be used, such as DetectNet object detectionarchitecture from nVidia.

At 301, training images are provided to the system, e.g., visual imagesof a pipe interior that have one or more pipe features of interest. Byway of example, for detection of pipe features, training images mayinclude images that contain one or more pipe feature, e.g., a crackand/or exposed rebar, along with metadata for each of the pipefeature(s). In the training images provided at 301, for example, themetadata may include an object classification (e.g., type, such ascrack, root intrusion, sediment buildup, exposed rebar, etc.) and thelocation of the object within the image, e.g., pixel coordinates of thepipe feature(s) or of bounding boxes that contain the pipe feature(s).

The training images provided at 301 permit an embodiment to train aneural network to recognize pipe feature(s) of interest within theimages, e.g., pipe features 102 a, 102 b illustrated in FIG. 1(A-B). Byway of specific example, in the case of a DetectNet type model, a gridis formed for the image and a determination is made as to whether eachgrid contains a pipe feature of interest. This grid approach permits theprovision of a rectangular bounding box around the pipe feature, asillustrated in FIG. 1(A-B) and FIG. 2(A-B). This of course is anon-limiting example, and rectangular or grid based detection need notbe utilized.

By way of further example, steps 301 and 302 may be part of a supervisedcomputer vision learning process where defined input and output pairsare provided as the training images. For example, using the DetectNettechnique, input and output pairs of images and rectangular boundingboxes of training data are provided at 301 to train an algorithm tolearn predictions on subsequent input, e.g., new pipe interior imagescollected with a pipe inspection platform. For identifying pipe featureswithin pipe interior image, e.g., image 101 a, 101 b of FIG. 1(A-B),using bounding boxes determined by a neural network, training imagesprovided at 301 might include a number of images and data filesindicating coordinates for the pipe feature location(s) in that image.

The output of this process, as illustrated at 302, is a determination ofdetected pipe feature(s) within the images, e.g., pipe feature(s) is/arebeing detected or not being detected, for example as judged by acomparison to known feature data of the training images or byvisualizing a visible indication, e.g., a bounding box, etc. If pipefeatures are detected, as determined at 302, these may be validated aspart of the training process, as illustrated at 304. For example,validation at 304 may include manual review and confirmation of pipefeature detection and/or bounding box locations within the trainingimages. This may be an automated or manual process, or both. Forexample, an algorithm may compute if an identified bounding box for apipe feature matches a known location of the pipe feature in a testimage. If pipe features are not being detected, or not being detectedproperly, as determined at 302 and/or 304, update(s) or changes to thetraining images and/or the model (e.g., neural network) used to detectpipe feature(s) may be made. This permits iterative analysis of trainingdata and improvement in the precision and/or accuracy of automated pipefeature detection.

It should be noted that the exact type of classification used foridentifying pipe features and/or classifying or labeling the pipefeature types is not limited to the examples described in thisspecification. However, the type of model may influence the accuracy orusefulness of the automated image processing. That is, a deep learningneural network model, such as GoogLeNet or DetectNet, may be preferableover a feature-based technique (e.g., manually engineered features foreach region of an image of interest and training of a classifier).However, depending on the application, such feature based classifiers orother techniques may be acceptable or preferred, e.g., due to limitedcomputing resources being available, less accuracy being required, etc.

In addition to pipe feature detection and indication (e.g., with abounding box), or in lieu of such pipe feature detection and indication,an embodiment may classify or label images, e.g., with a pipe featureidentification or label. This pipe feature classification or labellingmay be applied at the image level (e.g., image is determined to includeone or more pipe features) or at the pipe feature level (e.g., a boundedpipe feature is determined to be a particular type or types). As shownin FIG. 3, an embodiment may further or alternatively be provided withtraining data for classifying images or detected pipe features withinthe images.

This process may begin with a training phase, as for example by theprovision of training images as illustrated at 305. Similar to pipefeature detection, illustrated at 302, an embodiment may determine ifpipe features are being accurately classified at 306. For example, atraining image provided at 305 may include an input image of a knownpipe feature as well as meta data identifying or classifying the imageas containing that particular feature. Moreover, the training imageprovided at 305 may include an image sub-part, e.g., part of an imageincluding a detected object located at 302, and a correct classificationtype for that object, e.g., pipe feature type, material, etc.

At 306, the classification model is evaluated, which again may include aneural network or other classifier, and an embodiment can continue totrain the model, e.g., as illustrated at 307, until the classificationmodel is performing classification at an adequate level for theapplication(s) in question. Once this occurs, the training phase may becompleted, as illustrated at 308. Again, the particular type of machinelearning model to be used for classification may be selected using avariety of factors, e.g., a desired accuracy, an available amount ofprocessing power, etc.

Referring to FIG. 4, illustrated is an example method of pipe featuredetection and classification according to an embodiment. As illustrated,at 401 a pipe inspection platform is operated. The pipe inspectionplatform may include an autonomous mobile pipe inspection robot, a sledtype arrangement or any other inspection platform that permits retrievalof sensor data, broadly understood to include visual image data, as wellas other data types collected from a variety of sensors. The sensor datais obtained from the pipe inspection platform at 402, e.g., visualimages or other sensor data is stored in memory, for example of a pipeinspection robot or in the memory of a remote device that is operativelycoupled to the pipe inspection platform.

Thereafter, in the example case of visual images, the images aresubjected to an automated analysis process at 403. For example, objectdetection and/or classification is performed, e.g., using a neuralnetwork or other classifier, to detect pipe features and/or to classifyor label the pipe features or images. The analysis performed at step 403may include object detection, object classification, or both. Theanalysis performed at 403 may be conducted locally, i.e., on the pipeinspection platform, or remotely, e.g., after sensor data is transmittedto a cloud device or remote server.

In one example analysis, it is determined at 404 if pipe feature(s) aredetected. If so, e.g., a seam crack or exposed rebar is identified as anobject within the image, an embodiment may indicate the pipe feature(s),e.g., draw a bounding box around the pipe feature, as for exampleillustrated at 405. Irrespective if any such pipe feature is detected,or even if a location of the pipe feature cannot be determined withadequate accuracy, an embodiment may classify the images as containingpipe feature(s), as illustrated at 406. Of course, if one or more pipefeatures are detected at 404, these may also be subject toclassification at 406.

At 406, an embodiment performs an automated classification of the imagesand/or the objects detected in order to classify the pipe feature(s)contained in the image data (or other sensor data). For example, for apipe feature detected at 404, image data associated with that object maybe supplied to a model or classifier to classify the pipe feature as aparticular type. Likewise, for an image for which an embodiment did notdetect a pipe feature (whether or not object detection was attempted),the image may be subjected to a model or classifier trained to classifyimage data in order to attempt a classification of the image ascontaining a pipe feature of a particular type. In some cases, a usermay manually annotate or identify a pipe feature or region of interestprior to subjecting the image data to classification at 406.

If one or more pipe features are classified, the pipe feature(s) areidentified at 407. For example, a visual indication may be provided witha label listing the type(s) of pipe features detected. In an embodiment,such labels or annotations may be visually associated with locations ofthe detected pipe feature, as for example illustrated in FIG. 2(A-B). Asillustrated in FIG. 4, image data may be displayed for a user to review.For example, an image may be displayed without any labelling (e.g.,labelling data as illustrated in FIG. 2(A-B). In contrast, for images inwhich pipe features are detected and/or classified, an image may bedisplayed with labeling data (again as illustrated in FIG. 2(A-B)).

Therefore, an embodiment facilitates automated analysis of pipeinspection data, and includes a capability to detect pipe features, forexample providing a visual such as a bounding box around the feature.Further, an embodiment provides for an automated image analysis in theform of classification, and thus can provide an output in which pipefeature(s) are annotated or labelled in the image. This improves theprocess of reviewing pipe inspection data by applying an automatedanalysis to the pipe inspection data. This may, for example, serve as afirst pass that flags certain parts of expansive pipe inspection datafor further human review, flags certain parts of a pipe network, e.g.,by correlating pipe inspection data with a physical pipe or pipesegment, for further analysis, repair, etc. Thus, a user may be providedwith a visual highlighting area(s) of a pipe network that contain acertain type of feature, e.g., wall erosion of more than 50% and havingexposed rebar visible, and the user may be able to display images of thefeatures with annotations, e.g., by clicking on a highlighted graphicwithin the pipe network illustration to display an image such asillustrated in FIG. 2A.

It should be noted that an embodiment may utilize a variety of imagedata and sensor data types in performing the automated analysesillustrated by way of example in FIG. 4. This sensor data may beutilized alone or in some combination. For example, a pipe featuredetection and/or classification process may be performed for two or moreimage types, e.g., visual images and terahertz images, for the same pipearea. This may improve the accuracy of pipe feature detection and/orpipe feature classification. For example, a visual image of a pipefeature such as exposed rebar may be detected and classified by a neuralnetwork with a 50% confidence level. However, analysis of a terahertzimage of that same pipe location may detect and classify an image ascontaining exposed metal associated with rebar, again at a 50%confidence level. Taken together, this may increase the confidence ofthe overall classification, i.e., a section of pipe includes wallerosion and exhibits exposed rebar.

Similarly, certain types of sensor data, e.g., terahertz data, may becombined to permit an embodiment to perform material identification,alone or in some combination with analysis of other sensor data. Again,by way of example, an embodiment may first identify a pipe featurelocation within a visual image, e.g., exposed rebar, which is visuallyidentified with a bounding box. Further, an embodiment may thereafteranalyze a terahertz image of the same area of the pipe interior toidentify material(s) associated with the feature, e.g., cement, oxidizedmetal, etc. Similar analyses may be performed for other feature typesusing other sensor data types, alone or in some combination.

It will be readily understood that certain embodiments can beimplemented using any of a wide variety of devices or combinations ofdevices. Referring to FIG. 5, an example device that may be used inimplementing one or more embodiments includes a computing device(computer) 510, for example included in a pipe inspection platformand/or another computer system, e.g., remote server or cloud computingdevice.

The computer 510 may execute program instructions or code configured tostore and analyze sensor data and perform other functionality of theembodiments, as described herein. Components of computer 510 mayinclude, but are not limited to, a processing unit 520, a system memory530, and a system bus 522 that couples various system componentsincluding the system memory 530 to the processing unit 520. The computer510 may include or have access to a variety of non-transitory computerreadable media. The system memory 530 may include non-transitorycomputer readable storage media in the form of volatile and/ornonvolatile memory devices such as read only memory (ROM) and/or randomaccess memory (RAM). By way of example, and not limitation, systemmemory 530 may also include an operating system, application programs,other program modules, and program data. For example, system memory 530may include application programs such as image object identification andclassification software and/or sensor operational software. Data may betransmitted by wired or wireless communication, e.g., from one computingdevice to another computing device.

A user can interface with (for example, enter commands and information)the computer 510 through input devices 540 such as a touch screen,keypad, etc. A monitor or other type of display screen or device canalso be connected to the system bus 522 via an interface, such asinterface 550. The computer 510 may operate in a networked ordistributed environment using logical connections to one or more otherremote computers or databases. The logical connections may include anetwork, such local area network (LAN) or a wide area network (WAN), butmay also include other networks/buses.

It should be noted that the various functions described herein may beimplemented using processor executable instructions stored on anon-transitory storage medium or device. A non-transitory storage devicemay be, for example, an electronic, electromagnetic, or semiconductorsystem, apparatus, or device, or any suitable combination of theforegoing. More specific examples of a non-transitory storage mediuminclude the following: a portable computer diskette, a hard disk, arandom-access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), a portablecompact disc read-only memory (CD-ROM), or any suitable combination ofthe foregoing. In the context of this document “non-transitory” includesall media except non-statutory signal media.

Program code embodied on a non-transitory storage medium may betransmitted using any appropriate medium, including but not limited towireless, wireline, optical fiber cable, RF, etc., or any suitablecombination of the foregoing.

Program code for carrying out operations may be written in anycombination of one or more programming languages. The program code mayexecute entirely on a single device, partly on a single device, as astand-alone software package, partly on single device and partly onanother device, or entirely on the other device. In some cases, thedevices may be connected through any type of connection or network,including a local area network (LAN) or a wide area network (WAN), orthe connection may be made through other devices (for example, throughthe Internet using an Internet Service Provider), through wirelessconnections, or through a hard wire connection, such as over a USBconnection.

Example embodiments are described herein with reference to the figures,which illustrate example methods, devices and program products accordingto various example embodiments. It will be understood that the actionsand functionality may be implemented at least in part by programinstructions. These program instructions may be provided to a processorof a device to produce a special purpose machine, such that theinstructions, which execute via a processor of the device implement thefunctions/acts specified.

It is worth noting that while specific blocks are used in the figures,and a particular ordering of blocks has been illustrated, these arenon-limiting examples. In certain contexts, two or more blocks may becombined, a block may be split into two or more blocks, or certainblocks may be re-ordered or re-organized or omitted as appropriate, asthe explicit illustrated examples are used only for descriptive purposesand are not to be construed as limiting.

As used herein, the singular “a” and “an” may be construed as includingthe plural “one or more” unless clearly indicated otherwise.

This disclosure has been presented for purposes of illustration anddescription but is not intended to be exhaustive or limiting. Manymodifications and variations will be apparent to those of ordinary skillin the art. The example embodiments were chosen and described in orderto explain principles and practical application, and to enable others ofordinary skill in the art to understand the disclosure for variousembodiments with various modifications as are suited to the particularuse contemplated.

Thus, although illustrative example embodiments have been describedherein with reference to the accompanying figures, it is to beunderstood that this description is not limiting and that various otherchanges and modifications may be affected therein by one skilled in theart without departing from the scope or spirit of the disclosure.

What is claimed is:
 1. A method, comprising: operating a mobile pipeinspection platform to obtain sensor data for the interior of a pipe;analyzing, using a processor, the sensor data using a trained model,wherein the trained model is trained using a dataset including sensordata of pipe interiors and one or more of: metadata identifying pipefeature locations contained within the sensor data of the dataset andmetadata classifying pipe features contained within the sensor data ofthe dataset; performing one or more of: identifying, using a processor,a pipe feature location within the sensor data; and classifying, using aprocessor, a pipe feature of the sensor data; and thereafter producing,using a processor, an output including an indication of the pipefeature.
 2. The method of claim 1, wherein the analyzing comprisesautomated object detection using the sensor data.
 3. The method of claim1, wherein the analyzing comprising receiving user input to select apipe feature.
 4. The method of claim 1, wherein the trained modelcomprises a trained neural network.
 5. The method of claim 4, whereinthe trained neural network comprises a neural network trained usingreference visual images with annotated pipe features or pipe featurelocations.
 6. The method of claim 5, wherein the annotated pipe featuresare annotated by a human operator.
 7. The method of claim 1, wherein thepipe feature is selected from the group of pipe features consisting of:a crack in a pipe wall, erosion in a pipe wall, sediment buildup in apipe wall, and an intrusion through a pipe wall.
 8. The method of claim7, further comprising obtaining additional pipe inspection data from themobile pipe inspection platform.
 9. The method of claim 8, wherein theadditional pipe inspection data is selected from the group consisting oflaser pipe inspection data, sonar pipe inspection data, infrared pipeinspection data, and terahertz pipe inspection data.
 10. The method ofclaim 8, wherein the classifying comprises a material identification ofthe pipe feature.
 11. A system, comprising: a mobile pipe inspectionplatform that comprises one or more sensors to obtain sensor data forthe interior of a pipe; a computer system operatively coupled to themobile pipe inspection platform and configured to: analyze the sensordata using a trained model, wherein the trained model is trained using adataset including sensor data of pipe interiors and one or more of:metadata identifying pipe feature locations contained within the sensordata of the dataset and metadata classifying pipe features containedwithin the sensor data of the dataset; perform one or more of: identifya pipe feature location within the sensor data; and classify a pipefeature of the sensor data; and thereafter produce an output includingan indication of the pipe feature.
 12. The system of claim 11, whereinthe analysis of the sensor data comprises automated objectidentification using the sensor data.
 13. The system of claim 11,wherein the analysis of the sensor data comprises receiving user inputto select a pipe feature.
 14. The system of claim 11, wherein thetrained model comprises a trained neural network.
 15. The system ofclaim 14, wherein the trained neural network comprises a neural networktrained using reference visual images with annotated pipe features orpipe feature locations.
 16. The system of claim 15, wherein theannotated pipe features are annotated by a human operator.
 17. Thesystem of claim 11, wherein the pipe feature is selected from the groupof pipe features consisting of: a crack in a pipe wall, erosion in apipe wall, sediment buildup in a pipe wall, and an intrusion through apipe wall.
 18. The system of claim 17, wherein the mobile pipeinspection platform provides additional pipe inspection data to thecomputer system.
 19. The system of claim 18, wherein the additional pipeinspection data is selected from the group consisting of laser pipeinspection data, sonar pipe inspection data, infrared pipe inspectiondata, and terahertz pipe inspection data.
 20. A product, comprising: anon-transitory storage device that stores code that is executable by aprocessor, the code comprising: code that analyzes sensor data obtainedfrom a mobile pipe inspection platform using a trained model, where thetrained model is trained using a dataset including sensor data of pipeinteriors and one or more of: metadata identifying pipe featurelocations contained within the sensor data of the dataset and metadataclassifying pipe features contained within the sensor data of thedataset; code that performs one or more of: identifying a pipe featurelocation within the sensor data; and classifying a pipe feature of thesensor data; and code that thereafter produces an output including anindication of the pipe feature.